基于软硬件协同设计的解释器指令分派方法

为了降低指令分派造成的运行开销以提高解释器的性能,提出了一种采用软硬件协同设计的解释器指令分派方法。其核心思想是在软件层面通过对指令分派表进行优化以消除了代价较高的地址常量加载操作,在硬件层面通过扩展处理器的访存指令进一步实现基于硬件支持的访存加速。软硬件协同设计可以最大限度地降低由指令分派引入的运行时开销,从而提升解释执行的效率。试验结果表明,该方法能够显著提升解释器的性能。对于SPECjvm98和Da Capo测试集,解释器总体性能提升了11.5%,且单项性能的最大提升幅度高达15.4%。该方法通用性强,实现代价低,适用于现代主流处理器平台上高性能解释器的设计和优化。     

软硬件协同设计和系统级仿真探索

简要介绍了系统级芯片设计的软硬件协同设计、协同仿真技术和SoC开发中系统级协同仿真的工具，并给出了一个在CCSS (CoCentric system studio)环境下完成软硬件协同仿真的实例。     

面向多处理器SoC设计的低功耗软硬件划分

提出了解决多处理器SoC的低功耗软硬件划分问题的方法--基于神经网络的禁忌搜索算法.其基本思想是:真实的生物神经元具有抑制重复激活的阻尼特性,这与禁忌搜索对重复搜索加以限制相类似,因此设计具有阻尼特性的神经网络实现禁忌搜索算法,受阻尼特性抑制的神经元对应禁忌活动.由于神经网络复杂的动态特性和禁忌搜索优秀的全局搜索能力,该算法能够有效地跳出局部最优解.对真实任务图的实验表明,与遗传算法相比,该算法不但具有搜索速度上的优势,而且所得到的绝大部分软硬件划分方案有更低的系统功耗.     

通用电机产品族协同设计的模糊优化

产品族设计是大规模定制中的核心内容,许多文献在假定产品特征确定情况下,采用平台参数和非平台参数,提出了大规模定制下的产品族设计优化方法。基于产品族设计理念,针对含有不确定特征的产品族设计中的一种基本类型——产品参数与产品功能之间的映射含有模糊系数的情形,建立一种对平台参数和非平台参数进行协同优化的模糊双层线性规划模型,利用有关模糊算法对模糊条件下的通用电机产品族协同设计进行优化。     

复杂产品的多学科鲁棒协同设计优化方法研究

为了处理好复杂产品各子系统之间的耦合关系以及各子系统的异构性问题,以协同优化(CO)算法为基础,结合系统不确定分析(SUA)方法和近似不确定传播(IUP)方法,构建了多学科鲁棒协同设计优化算法框架.在设计变量的不确定性能够被概率分布函数描述的情况下,此算法框架能够解决复杂产品的设计优化问题.通过对梳齿式微加速度计的多学科鲁棒协同优化设计算例的计算,验证了此算法在输入参数存在微小扰动的情况下能够有效提高设计解的鲁棒性.     

公路设计与仿真协同框架

现在随着人民群众的生活水平的上升,人们越来越重视公路设计与仿真协同框架的建构,公路设计是采用某种适合的方式方法合理地设计公路,而仿真协同框架的建构,就需要我们重视两个方面,即仿真和协同,仿真,也就是模拟真实,采用某种不常用手段来达到符合实际使用标准的目的,而协同,就是要在公路设计中,各个技术协同配合,共同建构一个合理的设计框架,这更需要我们认真仔细地去思考和讨论。     

嵌入式设备电源控制系统的CAN通信软硬件设计

为解决电源监控系统中存在的数字化程度低、实时性差、准确度和可靠性低等问题,在研究CAN协议的原理及应用技术的基础上,采用外挂Microchip MCP2515 CAN控制器的设计思想,完成基于AVR单片机的CAN通信软硬件设计,满足电源控制系统策略的需要。该文给出CAN总线的整体结构及控制器、收发器等组成部分硬件及软件的设计与实现,并对系统进行网络负载分析及实时性分析。实际运行表明:该系统可实现报文的发送、接收等功能,可靠性强、通信实时性好。另外,电气隔离电路的设计,进一步提高系统的抗干扰能力;同时,软件设计部分采用自顶向下的模块化设计方法,增强系统的可移植性。     

基于FPGA的软硬件协同的多表哈希连接加速器

多表连接操作难以实现硬件加速。一方面,多表连接请求中表的数目不确定且连接方式多变,这种灵活的计算请求与固定的硬件行为之间存在矛盾;另一方面,多表连接的中间结果随表的增加而扩充,数据结构的管理和维护也要求更高的硬件开销。为支持灵活高效的多表连接计算,本文提出一种软硬件协同的优化方法。软件部分,将多表连接抽象为正向和反向2种计算模式并支持不同方式的多表连接。硬件设计采用访存和计算协同优化的方法:设计一种规则的硬件哈希表结构以提高内存访存带宽;设计支持正反向计算的同构专用计算引擎,配置多数据通道和指令控制系统实现高效的并行运算,提升多表哈希连接的计算效率。实验结果表明,相比中央处理器(CPU)执行表连接操作,单计算引擎能够提升性能9.2～11.0倍。通过多路并行的技术,实现8路并行的多表哈希引擎,能够充分利用板卡片外(DDR)内存带宽,实现相比CPU超过71.1倍的性能提升。     

面向设计教育的产学研协同创新的云服务平台设计研究

目的探讨互联网云服务平台发展背景下,设计教育产学研协同创新的模式和应用策略。方法在分析面向设计教育的产学研协同创新与云服务平台发展的基础上,构建基于云服务平台的设计教育产学研协同创新模式。结论以基于云服务平台的设计教育产学研协同创新模式为基础,结合湖南大学与华为技术有限公司联合研发的软件开发云项目案例,以及在湖南大学人机交互设计课程中的实践,应用互联网云服务平台促进设计教育的产学研协同创新,验证面向设计教育的产学研协同创新云服务平台的可行性。     

An analytical method for reliability analysis of hardware‐software co‐design system

Hardware‐software co‐design systems abound in diverse modern application areas such as automobile control, telecommunications, big data processing, and cloud computing. Existing works on reliability modeling of the co‐design systems have mostly assumed that hardware and software subsystems behave independently of each other. However, these two subsystems may have significant interactions in practice. In this paper, an analytical approach based on paths and integrals is proposed to analyze reliability of nonrepairable hardware‐software co‐design systems considering interactions between hardware and software during the system performance degradation and failure process. The proposed approach is verified using the Markov‐based method. As demonstrated by case studies on systems without and with warm standby sparing, the proposed approach is applicable to arbitrary types of time‐to‐failure or degradation distributions. Effects of different transition and fault detection/recovery parameters on system performance are also investigated through examples.     

A multiple-design-point approach for reliability-based design optimization

The application of design-point-based reliability-based design optimization (RBDO) methods is hindered by the challenge of multiple-design-point problems. In this article, to improve the commonality of design-point-based RBDO methods, a novel multiple-design-point (MDP) approach is developed. The MDP approach uses the trace of the design points from consequent reliability analysis iterations to identify whether there are multiple design points, then all of the design points are used to calculate shifting vectors for the sequential optimization and reliability assessment method, and the corresponding probabilistic constraints are moved to the feasible region along these multiple shifting vectors at the same time. With multiple shifted probabilistic constraints, the design feasibility associated with this probabilistic constraint will be satisfied. Two mathematical examples, a speed reducer design and a honeycomb crashworthiness design, are presented to validate the effectiveness of the MDP method. The results show that the MDP approach is effective for handling multiple-design-point problems.     

Novel collaborative optimization framework with a negotiation model for satellite system design

This article proposes a novel collaborative optimization (NCO) framework that uses a persuasive multi-agent negotiation method for satellite system design. Satellite system design is a typical multi-disciplinary design optimization problem. The traditional collaborative optimization (CO) is a competitive method but has the disadvantage of inefficient convergence caused by the interdisciplinary compatibility constraints in system-level optimization. By introducing the multi-agent negotiation method, an NCO framework is proposed, in which the system-level and subsystem-level variable negotiations replace the system-level compatibility constraints in traditional CO. The negotiation introduces a modification for candidate values that do not satisfy the compatibility constraints, ensures that an alternative compatible candidate will be obtained and has high convergence efficiency. Two numerical benchmark functions and a multi-disciplinary satellite system design are used to analyse the performance of the proposed NCO. The simulation results show that NCO can efficiently ensure parameter interdisciplinary matching and advanced convergence performance.     

A framework for parallelized efficient global optimization with application to vehicle crashworthiness optimization

This article presents a framework for simulation-based design optimization of computationally expensive problems, where economizing the generation of sample designs is highly desirable. One popular approach for such problems is efficient global optimization (EGO), where an initial set of design samples is used to construct a kriging model, which is then used to generate new ‘infill’ sample designs at regions of the search space where there is high expectancy of improvement. This article attempts to address one of the limitations of EGO, where generation of infill samples can become a difficult optimization problem in its own right, as well as allow the generation of multiple samples at a time in order to take advantage of parallel computing in the evaluation of the new samples. The proposed approach is tested on analytical functions, and then applied to the vehicle crashworthiness design of a full Geo Metro model undergoing frontal crash conditions.     

A unified parametric design approach to structural shape optimization

This paper presents a general parametric design approach for 2-D shape optimization problems. This approach has been achieved by integrating practical design methodologies into numerical procedures. It is characterized by three features: (i) automatic selection of a minimum number of shape design variables based on the CAD geometric model; (ii) integration of sequential convex programming algorithms to solve equality constrained optimization problems; (iii) efficient sensitivity analysis by means of the improved semi-analytical method. It is shown that shape design variables can be either manually or systematically identified with the help of equality constraints describing the relationship between geometric entities. Numerical solutions are performed to demonstrate the applicability of the proposed approach. A discussion of the results is also given:     

An efficient framework for the reliability-based design optimization of large-scale uncertain and stochastic linear systems

This paper is focused on the development of an efficient reliability-based design optimization algorithm for solving problems posed on uncertain linear dynamic systems characterized by large design variable vectors and driven by non-stationary stochastic excitation. The interest in such problems lies in the desire to define a new generation of tools that can efficiently solve practical problems, such as the design of high-rise buildings in seismic zones, characterized by numerous free parameters in a rigorously probabilistic setting. To this end a novel decoupling approach is developed based on defining and solving a limited sequence of deterministic optimization sub-problems. In particular, each sub-problem is formulated from information pertaining to a single simulation carried out exclusively in the current design point. This characteristic drastically limits the number of simulations necessary to find a solution to the original problem while making the proposed approach practically insensitive to the size of the design variable vector. To demonstrate the efficiency and strong convergence properties of the proposed approach, the structural system of a high-rise building defined by over three hundred free parameters is optimized under non-stationary stochastic earthquake excitation.     

A topological optimization approach for structural design of a high‐speed low‐load mechanism using the equivalent static loads method

In high‐speed low‐load mechanisms, the principal loads are the inertial forces caused by the high accelerations and velocities. Hence, mechanical design should consider lightweight structures to minimize such loads. In this paper, a topological optimization method is presented on the basis of the equivalent static loads method. Finite element (FE) models of the mechanism in different positions are constructed, and the equivalent loads are obtained using flexible multibody dynamics simulation. Kinetic DOFs are used to simulate the motion joints, and a quasi‐static analysis is performed to obtain the structural responses. The element sensitivity is calculated according to the static‐load‐equivalent equilibrium, in such a way that the influence on the inertial force is considered. A dimensionless component sensitivity factor (strain energy caused by unit load divided by kinetic energy from unit velocity) is used, which quantifies the significance of each element. Finally, the topological optimization approach is presented on the basis of the evolutionary structural optimization method, where the objective is to find the maximum ratio of strain energy to kinetic energy. In order to show the efficiency of the presented method, we presented two numerical cases. The results of these analyses show that the presented method is more efficient and can be easily implemented in commercial FE analysis software. Copyright © 2011 John Wiley & Sons, Ltd.     

A versatile software tool for microwave planar radar absorbing materials design using global optimization algorithms

A computer-aided design (CAD) tool for the design of planar multi-layer coatings with high absorption for a desired frequency and angle range is presented. The tool uses deterministic and evolutionary optimization design methods. Both single and multi-objective design algorithms can be used and a single absorber design or the Pareto front can be found accordingly. A novel design technique utilizing PSO is also presented. A user-defined or a pre-defined design case can be selected interchangeably. The choice of selecting materials from pre-defined database is also available. The tool can be useful for both educational and research purposes. The efficiency of the tool is demonstrated through several design cases that are in agreement with existing literature data.     

Al/CFRP混合薄壁结构耐撞性能可靠性优化设计

轻量化是实现汽车产业向安全、节能、环保发展的一个重要途径。Al/CFRP（carbon fiber reinforced plastic,碳纤维增强复合材料）混合材料能够在提升轻量化效果的同时兼顾材料成本和结构耐撞性能。为探索方形截面Al/CFRP混合薄壁结构的最佳组合方式,首先,制备了Al方管、CFRP方管和Al/CFRP混合方管,并开展准静态压溃实验。然后,建立能够精确模拟Al/CFRP混合方管压溃响应的有限元模型。最后,将试验设计方法、代理模型技术、多目标优化算法和蒙特卡罗模拟技术相结合,对Al/CFRP混合方管分别进行多目标确定性与可靠性优化设计,并对效果较好的可靠性优化解进行仿真验证。准静态压溃实验结果表明,Al/CFRP混合方管具有优异的耐撞性能;优化结果表明,可靠性优化解的约束可靠度相比于确定性优化解提高了10.96%,大大降低了失效概率,具有更强的实用性。研究结果有望对Al/CFRP混合薄壁吸能构件的优化设计提供参考。